Dorian821 Dorian821 - 1 month ago 24
Python Question

to_datetime Value Error: at least that [year, month, day] must be specified Pandas

I am reading from two different CSVs each having date values in their columns. After read_csv I want to convert the data to datetime with the to_datetime method. The formats of the dates in each CSV are slightly different, and although the differences are noted and specified in the to_datetime format argument, the one converts fine, while the other returns the following value error.

ValueError: to assemble mappings requires at least that [year, month, day] be sp
ecified: [day,month,year] is missing


first dte.head()

0 10/14/2016 10/17/2016 10/19/2016 8/9/2016 10/17/2016 7/20/2016
1 7/15/2016 7/18/2016 7/20/2016 6/7/2016 7/18/2016 4/19/2016
2 4/15/2016 4/14/2016 4/18/2016 3/15/2016 4/18/2016 1/14/2016
3 1/15/2016 1/19/2016 1/19/2016 10/19/2015 1/19/2016 10/13/2015
4 10/15/2015 10/14/2015 10/19/2015 7/23/2015 10/14/2015 7/15/2015


this dataframe converts fine using the following code:

dte = pd.to_datetime(dte, infer_datetime_format=True)


or

dte = pd.to_datetime(dte[x], format='%m/%d/%Y')


the second dtd.head()

0 2004-01-02 2004-01-02 2004-01-09 2004-01-16 2004-01-23 2004-01-30
1 2004-01-05 2004-01-09 2004-01-16 2004-01-23 2004-01-30 2004-02-06
2 2004-01-06 2004-01-09 2004-01-16 2004-01-23 2004-01-30 2004-02-06
3 2004-01-07 2004-01-09 2004-01-16 2004-01-23 2004-01-30 2004-02-06
4 2004-01-08 2004-01-09 2004-01-16 2004-01-23 2004-01-30 2004-02-06


this csv doesn't convert using either:

dtd = pd.to_datetime(dtd, infer_datetime_format=True)


or

dtd = pd.to_datetime(dtd, format='%Y-%m-%d')


It returns the value error above. Interestingly, however, using the parse_dates and infer_datetime_format as arguments of the read_csv method work fine. What is going on here?

Answer

For me works apply function to_datetime:

print (dtd)
            1           2           3           4           5           6
0                                                                        
0  2004-01-02  2004-01-02  2004-01-09  2004-01-16  2004-01-23  2004-01-30
1  2004-01-05  2004-01-09  2004-01-16  2004-01-23  2004-01-30  2004-02-06
2  2004-01-06  2004-01-09  2004-01-16  2004-01-23  2004-01-30  2004-02-06
3  2004-01-07  2004-01-09  2004-01-16  2004-01-23  2004-01-30  2004-02-06
4  2004-01-08  2004-01-09  2004-01-16  2004-01-23  2004-01-30  2004-02-06


dtd = dtd.apply(pd.to_datetime)

print (dtd)
           1          2          3          4          5          6
0                                                                  
0 2004-01-02 2004-01-02 2004-01-09 2004-01-16 2004-01-23 2004-01-30
1 2004-01-05 2004-01-09 2004-01-16 2004-01-23 2004-01-30 2004-02-06
2 2004-01-06 2004-01-09 2004-01-16 2004-01-23 2004-01-30 2004-02-06
3 2004-01-07 2004-01-09 2004-01-16 2004-01-23 2004-01-30 2004-02-06
4 2004-01-08 2004-01-09 2004-01-16 2004-01-23 2004-01-30 2004-02-06
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